When to Use

User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.

Architecture

Memory lives in

~/self-improving/

with tiered structure. If

~/self-improving/

does not exist, run

setup.md

~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── projects/          # Per-project learnings
├── domains/           # Domain-specific (code, writing, comms)
├── archive/           # COLD: decayed patterns
└── corrections.md     # Last 50 corrections log

Quick Reference

Topic	File
Setup guide	`setup.md`
Memory template	`memory-template.md`
Learning mechanics	`learning.md`
Security boundaries	`boundaries.md`
Scaling rules	`scaling.md`
Memory operations	`operations.md`
Self-reflection log	`reflections.md`

Detection Triggers

Log automatically when you notice these patterns:

Corrections → add to

corrections.md

, evaluate for

memory.md

"No, that's not right..."
"Actually, it should be..."
"You're wrong about..."
"I prefer X, not Y"
"Remember that I always..."
"I told you before..."
"Stop doing X"
"Why do you keep..."

Preference signals → add to

memory.md

if explicit:

"I like when you..."
"Always do X for me"
"Never do Y"
"My style is..."
"For [project], use..."

Pattern candidates → track, promote after 3x:

Same instruction repeated 3+ times
Workflow that works well repeatedly
User praises specific approach

Ignore (don't log):

One-time instructions ("do X now")
Context-specific ("in this file...")
Hypotheticals ("what if...")

Self-Reflection

After completing significant work, pause and evaluate:

Did it meet expectations? — Compare outcome vs intent
What could be better? — Identify improvements for next time
Is this a pattern? — If yes, log to
```
corrections.md
```

When to self-reflect:

After completing a multi-step task
After receiving feedback (positive or negative)
After fixing a bug or mistake
When you notice your output could be better

Log format:

CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]

Example:

CONTEXT: Building Flutter UI
REFLECTION: Spacing looked off, had to redo
LESSON: Check visual spacing before showing user

Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.

Quick Queries

User says	Action
"What do you know about X?"	Search all tiers for X
"What have you learned?"	Show last 10 from `corrections.md`
"Show my patterns"	List `memory.md` (HOT)
"Show [project] patterns"	Load `projects/{name}.md`
"What's in warm storage?"	List files in `projects/` + `domains/`
"Memory stats"	Show counts per tier
"Forget X"	Remove from all tiers (confirm first)
"Export memory"	ZIP all files

Memory Stats

On "memory stats" request, report:

📊 Self-Improving Memory
HOT (always loaded):
memory.md: X entries
WARM (load on demand):
projects/: X files
domains/: X files
COLD (archived):
archive/: X files
Recent activity (7 days):
Corrections logged: X
Promotions to HOT: X
Demotions to WARM: X

Core Rules

1. Learn from Corrections and Self-Reflection

Log when user explicitly corrects you
Log when you identify improvements in your own work
Never infer from silence alone
After 3 identical lessons → ask to confirm as rule

2. Tiered Storage

Tier	Location	Size Limit	Behavior
HOT	memory.md	≤100 lines	Always loaded
WARM	projects/, domains/	≤200 lines each	Load on context match
COLD	archive/	Unlimited	Load on explicit query

3. Automatic Promotion/Demotion

Pattern used 3x in 7 days → promote to HOT
Pattern unused 30 days → demote to WARM
Pattern unused 90 days → archive to COLD
Never delete without asking

4. Namespace Isolation

Project patterns stay in
```
projects/{name}.md
```
Global preferences in HOT tier (memory.md)
Domain patterns (code, writing) in
```
domains/
```
Cross-namespace inheritance: global → domain → project

5. Conflict Resolution

When patterns contradict:

Most specific wins (project > domain > global)
Most recent wins (same level)
If ambiguous → ask user

6. Compaction

When file exceeds limit:

Merge similar corrections into single rule
Archive unused patterns
Summarize verbose entries
Never lose confirmed preferences

7. Transparency

Every action from memory → cite source: "Using X (from projects/foo.md:12)"
Weekly digest available: patterns learned, demoted, archived
Full export on demand: all files as ZIP

8. Security Boundaries

See

boundaries.md

— never store credentials, health data, third-party info.

9. Graceful Degradation

If context limit hit:

Load only memory.md (HOT)
Load relevant namespace on demand
Never fail silently — tell user what's not loaded

Scope

This skill ONLY:

Learns from user corrections and self-reflection
Stores preferences in local files (
```
~/self-improving/
```
)
Reads its own memory files on activation

This skill NEVER:

Accesses calendar, email, or contacts
Makes network requests
Reads files outside
```
~/self-improving/
```
Infers preferences from silence or observation
Modifies its own SKILL.md

Related Skills

Install with

clawhub install <slug>

if user confirms:

```
memory
```
— Long-term memory patterns for agents
```
learning
```
— Adaptive teaching and explanation
```
decide
```
— Auto-learn decision patterns
```
escalate
```
— Know when to ask vs act autonomously

Feedback

If useful:
```
clawhub star self-improving
```
Stay updated:
```
clawhub sync
```

Self Improving 1.2.10

AI Skill Market Insights

Be Part of the 0+ Developer Community

When to Use

Architecture

Quick Reference

Detection Triggers

Self-Reflection

Quick Queries

Memory Stats

Core Rules

1. Learn from Corrections and Self-Reflection

2. Tiered Storage

3. Automatic Promotion/Demotion

4. Namespace Isolation

5. Conflict Resolution

6. Compaction

7. Transparency

8. Security Boundaries

9. Graceful Degradation

Scope

Related Skills

Feedback

Quick Start

Manual Installation

TEAR & SHARE

Tags

Multi-Agent Patterns

Memory Systems Design

Context Degradation Detection

Context Optimization

Tool Design for Agents

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